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UID:pretalx-foss4g-2022-3Z3TQY@talks.staging.osgeo.org
DTSTART;TZID=CET:20220826T141500
DTEND;TZID=CET:20220826T144500
DESCRIPTION:The Scientific and Technical Center for Building (CSTB) built t
 he first French database of buildings and houses to address climate change
  challenge\, helping knowledge and decision making for massive retrofit.  
 \nThe pipeline factory intersects massive datasets (21 Millions buildings\
 , >400 descriptors) and keeps adding new predictions and external datasets
  all the time. It allows to run analyses and predictions for all the clima
 te change related indicators\, such as housing price and energetic perform
 ance relation\, heat wave impact\, solar potential\, etc.. \nWhile the fir
 st versions where a direct image of the classical datascientist’s approa
 ch -ie a massive dataframe driven by massive yaml config files and cryptic
  meta-templated scripts– ease of use and access performance soon became 
 a limiting factor.  This is a major concern since this dataset will be one
  long term foundation of derived information systems. \nBetween brute forc
 e approach based on scaling resources up\, and the old fashioned « data 
 diet » normalization and optimization process\, the truth is not easy to
  find.  \nAbusing from cartoonish humor\, this talk will try to explore th
 e benefits of normalizing back hugely redundant geographic datasets and ma
 king public interfaces (public SQL model\, API’s\, vector tiles\, OGC AP
 I’s) so that both end users can analyze efficiently this dataset\, and t
 he data manager team can rely on more stability using those good old’ da
 tabase constraints.
DTSTAMP:20260404T104317Z
LOCATION:Room Limonaia
SUMMARY:How to deal with a massive geographic database when surrounded by d
 atascientists ? - Régis Haubourg
URL:https://talks.staging.osgeo.org/foss4g-2022/talk/3Z3TQY/
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